Diabetes Bayesian Network with 413 Nodes and 602 Arcs

A Bayesian network model for diabetes with 413 nodes and 602 arcs, containing 429,409 parameters. The model was developed by S. Andreassen, R. Hovorka, J. Benn, K. G. Olesen, and E. R. Carson. The work was presented at the 3rd Conference on Artificial Intelligence in Medicine in 1991.

Use Cases

• Probabilistic inference for diabetes-related variables based on the network structure.
• Causal reasoning and sensitivity analysis based on the model's parameters.
• Benchmarking structure learning algorithms based on the known network topology.
• Educational demonstrations of Bayesian networks in a medical context based on the discrete variables.

Strengths

• Well-defined network structure with 413 nodes and 602 arcs.
• Large parameter space of 429,409 discrete parameters.
• Clear academic provenance with a cited 1991 conference paper.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

bnlearn Bayesian Network Repository, based on work by S. Andreassen et al.

Collection Method

Model-based approach to insulin adjustment, likely derived from clinical knowledge.

Time Range

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Freshness

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Geography

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